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@article{DBLP:journals/corr/PlappertHDSCCAA17,
	Author = {Matthias Plappert and Rein Houthooft and Prafulla Dhariwal and Szymon Sidor and Richard Y. Chen and Xi Chen and Tamim Asfour and Pieter Abbeel and Marcin Andrychowicz},
	Date-Added = {2017-11-09 06:16:18 +0000},
	Date-Modified = {2017-11-09 06:21:39 +0000},
	Journal = {CoRR},
	Title = {{Parameter Space Noise for Exploration}},
	Volume = {abs/1706.01905},
	Year = {2017}}

@article{DBLP:journals/corr/LillicrapHPHETS15,
	Author = {Timothy P. Lillicrap and Jonathan J. Hunt and Alexander Pritzel and Nicolas Heess and Tom Erez and Yuval Tassa and David Silver and Daan Wierstra},
	Date-Added = {2017-11-09 06:02:38 +0000},
	Date-Modified = {2017-11-09 06:22:34 +0000},
	Journal = {CoRR},
	Title = {{Continuous Control with Deep Reinforcement Learning}},
	Volume = {abs/1509.02971},
	Year = {2015}}

@misc{cs229_stanford_portfolio,
	Author = {Olivier Jin and Hamza El-Saawy},
	Date-Added = {2017-10-17 04:47:40 +0000},
	Date-Modified = {2017-11-28 09:22:39 +0000},
	Howpublished = {http://cs229.stanford.edu/proj2016/report/JinElSaawy-PortfolioManagementusingReinforcementLearning-report.pdf},
	Title = {Portfolio Management using Reinforcement Learning}}

@article{DBLP:journals/corr/JiangXL17,
	Author = {Zhengyao Jiang and Dixing Xu and Jinjun Liang},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/JiangXL17},
	Date-Added = {2017-10-17 04:46:28 +0000},
	Date-Modified = {2017-11-09 06:22:52 +0000},
	Journal = {CoRR},
	Timestamp = {Mon, 17 Jul 2017 14:26:05 +0200},
	Title = {{A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem}},
	Url = {http://arxiv.org/abs/1706.10059},
	Volume = {abs/1706.10059},
	Year = {2017},
	Bdsk-Url-1 = {http://arxiv.org/abs/1706.10059}}

@misc{cs229_stanford_trading,
	Author = {Xin Du and Jinjian Zhai and Koupin Lv},
	Date-Added = {2017-10-17 04:38:55 +0000},
	Date-Modified = {2017-10-17 04:48:52 +0000},
	Howpublished = {http://cs229.stanford.edu/proj2009/LvDuZhai.pdf},
	Title = {Algorithm Trading Using Q-Learning and Recurrent Reinforcement Learning}}

@misc{Ding_usingstructured,
	Author = {Xiao Ding and Yue Zhang and Ting Liu and Junwen Duan},
	Date-Added = {2017-10-16 17:43:29 +0000},
	Date-Modified = {2017-10-16 17:43:29 +0000},
	Title = {Using Structured Events to Predict Stock Price Movement: An Empirical Investigation}}

@inproceedings{semantic_frames,
	Address = {Sofia, Bulgaria},
	Author = {Xie, Boyi and Passonneau, Rebecca J. and Wu, Leon and Creamer, Germ\'{a}n G.},
	Booktitle = {Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
	Date-Added = {2017-10-16 17:41:50 +0000},
	Date-Modified = {2017-10-16 17:41:50 +0000},
	Month = {August},
	Pages = {873--883},
	Publisher = {Association for Computational Linguistics},
	Title = {Semantic Frames to Predict Stock Price Movement},
	Url = {http://www.aclweb.org/anthology/P13-1086},
	Year = {2013},
	Bdsk-Url-1 = {http://www.aclweb.org/anthology/P13-1086}}

@inbook{social_relation_sentiment_analysis,
	Author = {Jianfeng Si and Arjun Mukherjee and Bing Liu and Pan, {Sinno Jialin} and Qing Li and Huayi Li},
	Booktitle = {EMNLP 2014 - 2014 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference},
	Date-Added = {2017-10-16 17:30:09 +0000},
	Date-Modified = {2017-10-16 17:30:09 +0000},
	Pages = {1139--1145},
	Publisher = {Association for Computational Linguistics (ACL)},
	Title = {Exploiting social relations and sentiment for stock prediction},
	Year = {2014}}

@inbook{topic_based,
	Abstract = {This paper proposes a technique to leverage topic based sentiments from Twitter to help predict the stock market. We first utilize a continuous Dirichlet Process Mixture model to learn the daily topic set. Then, for each topic we derive its sentiment according to its opinion words distribution to build a sentiment time series. We then regress the stock index and the Twitter sentiment time series to predict the market. Experiments on real-life S&P100 Index show that our approach is effective and performs better than existing state-of-The-art non-topic based methods.},
	Author = {Jianfeng Si and Arjun Mukherjee and Bing Liu and Qing Li and Huayi Li and Xiaotie Deng},
	Booktitle = {ACL 2013 - 51st Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference},
	Date-Added = {2017-10-16 17:27:44 +0000},
	Date-Modified = {2017-10-16 17:27:44 +0000},
	Isbn = {9781937284510},
	Month = {1},
	Pages = {24--29},
	Publisher = {Association for Computational Linguistics (ACL)},
	Title = {Exploiting topic based twitter sentiment for stock prediction},
	Volume = {2},
	Year = {2013}}

@article{twitter_mode,
	Author = {Johan Bollen and Huina Mao and Xiao{-}Jun Zeng},
	Bibsource = {dblp computer science bibliography, http://dblp.org},
	Biburl = {http://dblp.uni-trier.de/rec/bib/journals/corr/abs-1010-3003},
	Date-Added = {2017-10-16 17:20:15 +0000},
	Date-Modified = {2017-10-16 17:20:15 +0000},
	Journal = {CoRR},
	Timestamp = {Wed, 07 Jun 2017 14:41:29 +0200},
	Title = {Twitter mood predicts the stock market},
	Url = {http://arxiv.org/abs/1010.3003},
	Volume = {abs/1010.3003},
	Year = {2010},
	Bdsk-Url-1 = {http://arxiv.org/abs/1010.3003}}

@inproceedings{event_driven,
	Acmid = {2832572},
	Author = {Ding, Xiao and Zhang, Yue and Liu, Ting and Duan, Junwen},
	Booktitle = {Proceedings of the 24th International Conference on Artificial Intelligence},
	Date-Added = {2017-10-16 17:11:02 +0000},
	Date-Modified = {2017-10-16 17:11:02 +0000},
	Isbn = {978-1-57735-738-4},
	Location = {Buenos Aires, Argentina},
	Numpages = {7},
	Pages = {2327--2333},
	Publisher = {AAAI Press},
	Series = {IJCAI'15},
	Title = {Deep Learning for Event-driven Stock Prediction},
	Url = {http://dl.acm.org/citation.cfm?id=2832415.2832572},
	Year = {2015},
	Bdsk-Url-1 = {http://dl.acm.org/citation.cfm?id=2832415.2832572}}
